Search Results (234)

Photoplethysmographic (PPG) sensors are small and cheap wearable sensors which open the possibility of monitoring physiological parameters such as heart rate during normal daily routines, ultimately providing valuable information on health status. Despite their potential and distribution within wearable devices, their accuracy is affected by several influencing parameters, such as contact pressure and physical activity. In this study, the effect of contact pressure (i.e., at 20, 60, and 75 mmHg) and intensity of physical activity (i.e., at 3, 6, and 8 km/h) were evaluated on a sample of 25 subjects using both a reference device (i.e., an electrocardiography-based device) and a PPG sensor applied to the skin with controlled contact pressure values. Results showed differing accuracy and precision when measuring the heart rate at different pressure levels, achieving the best performance at a contact pressure of 60 mmHg, with a mean absolute percentage error of between 3.36% and 6.83% depending on the physical activity levels, and a Pearson’s correlation coefficient of between 0.81 and 0.95. Plus, considering the individual optimal contact pressure, measurement uncertainty significantly decreases at any contact pressure, for instance, decreasing from 15 bpm (at 60 mmHg) to 8 bpm when running at a speed of 6 km/h (coverage factor k = 2). These results may constitute useful information for both users and manufacturers to improve the metrological performance of PPG sensors and expand their use in a clinical context. Full article

Objectives: With the growing importance of mobile technology and artificial intelligence (AI) in healthcare, the development of automated cardiac diagnostic systems has gained strategic significance. This review aims to summarize the current state of knowledge on the use of AI in the analysis of electrocardiographic (ECG) signals obtained from wearable devices, particularly smartwatches, and to outline perspectives for future clinical applications. Methods: A narrative literature review was conducted using PubMed, Web of Science, and Scopus databases. The search focused on combinations of keywords related to AI, ECG, and wearable technologies. After screening and applying inclusion criteria, 152 publications were selected for final analysis. Conclusions: Modern AI algorithms—especially deep neural networks—show promise in detecting arrhythmias, heart failure, prolonged QT syndrome, and other cardiovascular conditions. Smartwatches without ECG sensors, using photoplethysmography (PPG) and machine learning, show potential as supportive tools for preliminary atrial fibrillation (AF) screening at the population level, although further validation in diverse real-world settings is needed. This article explores innovation trends such as genetic data integration, digital twins, federated learning, and local signal processing. Regulatory, technical, and ethical challenges are also discussed, along with the issue of limited clinical evidence. Artificial intelligence enables a significant enhancement of personalized, mobile, and preventive cardiology. Its integration into smartwatch ECG analysis opens a path toward early detection of cardiac disorders and the implementation of population-scale screening approaches. Full article

This study proposes a novel heart rate estimation method that detects subtle cardiac-induced vibrations propagated through the cardiovascular system based on the ballistocardiography (BCG) principle, using a six-axis heart rate sensing device that integrates a three-axis accelerometer and a three-axis gyroscope. To validate the effectiveness of the proposed method, a comparative analysis was conducted against heart rate measurements obtained from photoplethysmography (PPG) sensors, which are widely used in conventional heart rate monitoring. Experiments were conducted on 20 adult participants, and frequency domain analysis was performed using different time windows of 30 s, 20 s, 8 s, and 4 s. The results showed that the 4 s window provided the highest accuracy in heart rate estimation, demonstrating that the proposed method can effectively capture fine cardiac-induced vibrations. This approach offers a significant advantage by utilizing inertial sensors commonly embedded in wearable devices for heart rate monitoring without the need for additional optical sensors. Compared to optical-based systems, the proposed method is more power-efficient and less affected by environmental factors such as ambient lighting conditions. The findings suggest that heart rate estimation using the six-axis heart rate sensing device presents a reliable, continuous, and non-invasive alternative for cardiovascular monitoring. Full article

Photoplethysmography (PPG) is a widely adopted optical technique for cardiovascular monitoring, but its accuracy is often compromised by skin pigmentation, which attenuates the signal in individuals with darker skin tones. This research addresses the challenge of skin pigmentation by developing a PPG sensor system with a novel gain calibration strategy. We present a hardware prototype integrating a programmable gain amplifier (PGA), specifically the OPA3S328 operational amplifier, controlled by a microcontroller. The system performs a one-time gain adjustment at initialization based on the user’s skin tone, which is quantified using RGB image analysis. This “set-and-hold” approach normalizes the signal amplitude across various skin tones while effectively preserving the native morphology of the PPG waveform, which is essential for advanced cardiovascular diagnostics. Experimental validation with over 70 human volunteers demonstrated the PGA’s ability to apply calibrated gain levels, derived from a first-degree polynomial relationship between skin pigmentation and red light absorption. This approach significantly improved signal consistency across different skin tones. The findings highlight the efficacy of pre-measurement gain correction for achieving reliable PPG sensing in diverse populations and lay the groundwork for future optimization of PPG sensor designs to improve reliability in wearable health monitoring devices. Full article

With advancing age, blood vessels undergo deterioration that causes structural and functional changes, including a progressive increase in arterial wall stiffness. Since arterial stiffness is closely linked to the potential risks of cardiovascular diseases, which remains the leading cause of global mortality, it has become essential to develop effective techniques for early diagnosis and continuous monitoring over time. Photoplethysmography, a low-cost and non-invasive technology that measures blood volume changes, has gained increasing popularity in recent years and has proven to be a potential valuable tool for estimating arterial stiffness. This study employs an in vitro experimental setup designed to simulate the cardiovascular system performing under controlled velocity and pressure conditions, in which silicone phantom models with different geometric and mechanical properties were implemented to evaluate their stiffness using a pair of photoplethysmographic sensors. These were employed to measure the pulse wave velocity, currently considered the reference technique for estimating arterial stiffness, correlated through the well-known Moens–Korteweg equation. Photoplethysmographic sensors were placed at three specific distances to determine an optimal configuration for assessing arterial stiffness. Results showed the best performance for softer vascular models at a 15 cm sensor distance, with measurements demonstrating satisfactory accuracy. Variability and standard deviation values increased with model stiffness. The aim of this study is to improve the use of photoplethysmographic sensors for monitoring the mechanical properties of blood vessels and, therefore, to prevent potential cardiovascular diseases. Full article

We present a hermetically sealed capacitive microelectromechanical system (MEMS) strain sensor designed for arterial pulse waveform extraction using the strain plethysmography (SPG) modality. The MEMS strain sensor features a small form factor of 3.3 mm × 3.3 mm × 1 mm, leverages a nano-gap fabrication process to improve the sensitivity, and uses a differential sensing mechanism to improve the linearity and remove the common mode drift. The MEMS strain sensor is interfaced with an application-specific integrated circuit (ASIC) to form a compact strain sensing system. This system exhibits a high strain sensitivity of 316 aF/µε, a gauge factor (GF) of 35, and a strain sensing resolution of 1.26 µε, while maintaining a linear range exceeding 700 µε. SPG signals have been reliably captured at both the fingertip and wrist using the MEMS strain sensor with high signal quality, preserving various photoplethysmography (PPG) features. Experimental results demonstrate that heart rate (HR) and heart rate variability (HRV) can be estimated from the SPG signal collected at the fingertip and wrist using the sensor with an accuracy of over 99%. Pulse arrival time (PAT) and pulse transit time (PTT) have been successfully extracted using the sensor together with a MEMS seismometer, showcasing its potential for ambulatory BP monitoring (ABPM) application. Full article

Background/Objectives: Atrial fibrillation (AF) remains a major risk factor for stroke. It is often asymptomatic and paroxysmal, making it difficult to detect with conventional electrocardiography (ECG). While photoplethysmography (PPG)-based devices like smartwatches have demonstrated efficacy in detecting AF, they are rarely integrated into hospital infrastructure. The study aimed to establish a seamless system for real-time AF screening in hospitalized high-risk patients using a wrist-worn PPG device integrated into a hospital’s data infrastructure. Methods: In this investigator-initiated prospective clinical trial conducted at the University Hospital Basel, patients with a CHA₂DS₂-VASc score ≥ 2 and no history of AF received a wristband equipped with a PPG sensor for continuous monitoring during their hospital stay. The PPG data were automatically transmitted, analyzed, stored, and visualized. Upon detection of an absolute arrhythmia (AA) in the PPG signal, a Holter ECG was administered. Results: The analysis encompassed 346 patients (mean age 72 ± 10 years, 175 females (50.6%), mean CHA₂DS₂-VASc score 3.5 ± 1.3)). The mean monitoring duration was 4.3 ± 4.4 days. AA in the PPG signal was detected in twelve patients (3.5%, CI: 1.5–5.4%), with most cases identified within 24 h (p = 0.004). There was a 1.3 times higher AA burden during the nighttime compared to daytime (p = 0.03). Compliance was high (304/346, 87.9%). No instances of AF were confirmed in the nine patients undergoing Holter ECG. Conclusions: This study successfully pioneered an automated infrastructure for AF screening in hospitalized patients through the use of wrist-worn PPG devices. This implementation allowed for real-time data visualization and intervention in the form of a Holter ECG. The high compliance and early AA detection achieved in this study underscore the potential and relevance of this novel infrastructure in clinical practice. Full article

(1) Background: Blood pressure (BP) variability is an important risk factor for cardiovascular diseases. Still, existing BP monitoring methods often require periodic cuff-based measurements, raising concerns about their accuracy and convenience. This study aims to develop a subject-independent, cuff-less BP estimation method using finger and toe photoplethysmography (PPG) signals combined with an electrocardiogram (ECG) without the need for an initial cuff-based measurement. (2) Methods: A customized measurement system was used to record 80 readings from human subjects. Fifteen features with the highest dependency on the reference BP, including time and morphological characteristics of PPG and subject information, were analyzed. A multivariate regression model was employed to estimate BP. (3) Results: The results showed that incorporating toe PPG signals improved the accuracy of BP estimation, reducing the mean absolute error (MAE). Using both finger and toe PPG signals resulted in an MAE of 9.63 ± 12.54 mmHg for systolic BP and 6.76 ± 8.38 mmHg for diastolic BP, providing the lowest MAE compared to previous methods. (4) Conclusions: This study is the first to integrate toe PPG for more accurate BP estimation and proposes a method that does not require an initial cuff-based BP measurement, offering a promising approach for non-invasive, continuous BP monitoring. Full article

Atrial fibrillation (AF) is a type of cardiac arrhythmia with a worldwide prevalence of more than 37 million among the adult population. This elusive disease is a major risk factor for ischemic stroke, along with increased rates of significant morbidity and eventual mortality. It is clinically diagnosed using medical-grade electrocardiogram (ECG) sensors in ambulatory settings. The recent emergence of consumer-grade wearables equipped with photoplethysmography (PPG) sensors has exhibited considerable promise for non-intrusive continuous monitoring in free-living conditions. However, the scarcity of large-scale public PPG datasets acquired from wearable devices hinders the development of intelligent automatic AF detection algorithms unaffected by motion artifacts, saturated ambient noise, inter- and intra-subject differences, or limited training data. In this work, we present a deep learning framework that leverages convolutional layers with a bidirectional long short-term memory (CNN-BiLSTM) network and an attention mechanism for effectively classifying raw AF rhythms from normal sinus rhythms (NSR). We derive and feed heart rate variability (HRV) and pulse rate variability (PRV) features as auxiliary inputs to the framework for robustness. A larger teacher model is trained using the MIT-BIH Arrhythmia ECG dataset. Through transfer learning (TL), its learned representation is adapted to a compressed student model (32x smaller) variant by using knowledge distillation (KD) for classifying AF with the UMass and MIMIC-III datasets of PPG signals. This results in the student model yielding average improvements in accuracy, sensitivity, F1 score, and Matthews correlation coefficient of 2.0%, 15.05%, 11.7%, and 9.85%, respectively, across both PPG datasets. Additionally, we employ Gradient-weighted Class Activation Mapping (Grad-CAM) to confer a notion of interpretability to the model decisions. We conclude that through a combination of techniques such as TL and KD, i.e., pre-trained initialization, we can utilize learned ECG concepts for scarcer PPG scenarios. This can reduce resource usage and enable deployment on edge devices. Full article

A photoplethysmography (PPG) pulse in reflection mode represents the change in diffuse reflectance at the skin surface during a cardiac cycle and is commonly used in wearable devices to monitor heart rate. Commercial PPG sensors often rely on the reflectance signal from light sources at two different wavelength regions, green, such as λ = 523 nm, and near infrared (NIR), such as λ = 945 nm. Early in vivo studies of wearable sensors showed that green light is more beneficial than NIR light in optimizing PPG sensitivity. This contradicts the common trends in the standard near infrared spectroscopy techniques, which rely on the long optical pathlengths at NIR wavelengths to achieve optimal depth sensitivity. To quantitatively analyze the spectral characteristics of PPG across the wavelength region of 500–900 nm in a controlled environment, this study performs the spectral measurement of PPG signals using a simple and cost-effective optical phantom model with two distinct layers and a customized diffuse reflectance spectroscopy system. In addition, Monte Carlo simulations are used to elaborate the underlying phenomena at the green and NIR wavelengths when considering different epithelial thicknesses and source–detector distances (SDD). Full article

Remote vital sensing in veterinary medicine is a relatively new area of practice, which involves the acquisition of data without invasion of the body cavities of live animals. This paper aims to review several technologies in remote vital sensing: infrared thermography, remote photoplethysmography (rPPG), radar, wearable sensors, and computer vision and machine learning. In each of these technologies, we outline its concepts, uses, strengths, and limitations in multiple animal species, and its potential to reshape health surveillance, welfare evaluation, and clinical medicine in animals. The review also provides information about the problems associated with applying these technologies, including species differences, external conditions, and the question of the reliability and classification of these technologies. Additional topics discussed in this review include future developments such as the use of artificial intelligence, combining different sensing methods, and creating monitoring solutions tailored to specific animal species. This contribution gives a clear understanding of the status and future possibilities of remote vital sensing in veterinary applications and stresses the importance of that technology for the development of the veterinary field in terms of animal health and science. Full article

A recent focus has been on developing wearable health solutions that allow users to seamlessly track their health metrics during their daily activities, providing convenient and continuous access to vital physiological data. This work investigates a heart rate (HR) monitoring system and compares the HR measurement from two potential sites for foot wearable technologies. The proposed system used a commercially available photoplethysmography sensor (PPG), microcontroller, Bluetooth module, and mobile phone application. HR measurements were obtained from two anatomical sites, i.e., the dorsalis pedis artery (DPA) and the posterior tibial artery (PTA), and compared to readings from the Apple Smartwatch during standing and walking tasks. The system was validated on twenty healthy volunteers, employing ANOVA and Bland-Altman analysis to assess the accuracy and consistency of the HR measurements. During the standing test, the Bland-Altman analysis showed a mean difference of 0.08 bpm for the DPA compared to a smaller mean difference of 0.069 bpm for the PTA. On the other hand, the walking test showed a mean difference of 0.255 bpm and −0.06 bpm for the DPA and PTA, respectively. These results showed a high level of agreement between the HR measurements collected at the foot with the smartwatch measurements, with superiority for the HR measurements collected at the PTA. Full article

The rate of oxygen consumption, which is measured as the volume of oxygen consumed per mass per minute (VO₂) mL/kg/min, is a critical metric for evaluating cardiovascular health, metabolic status, and respiratory function. Specifically, VO₂ is a powerful prognostic predictor of survival in patients with heart failure (HF) because it provides an indirect assessment of a patient’s ability to increase cardiac output (CO). In addition, VO₂ measurements, particularly VO₂ max, are significant because they provide a reliable indicator of your cardiovascular fitness and aerobic endurance. However, traditional VO₂ assessment requires bulky, breath-by-breath gas analysis systems, limiting frequent and continuous monitoring to specialized settings. This study presents a novel wrist-worn multiwavelength photoplethysmography (PPG) device and machine learning algorithm designed to estimate VO₂ continuously. Unlike conventional wearables that rely on static formulas for VO₂ max estimation, our algorithm leverages the data from the PPG wearable and uses the Beer–Lambert Law with inputs from five wavelengths (670 nm, 770 nm, 810 nm, 850 nm, and 950 nm), incorporating the isosbestic point at 810 nm to differentiate oxy- and deoxy-hemoglobin. A validation study was conducted with eight subjects using a modified Bruce protocol, comparing the PPG-based estimates to the gold-standard Parvo Medics gas analysis system. The results demonstrated a mean absolute error of 1.66 mL/kg/min and an R² of 0.94. By providing precise, individualized VO₂ estimates using direct tissue oxygenation data, this wearable solution offers significant clinical and practical advantages over traditional methods, making continuous and accurate cardiovascular assessment readily available beyond clinical environments. Full article

There exists a distinct lack of publicly available literature addressing the most effective hardware design for photoplethysmography (PPG) devices for clinical and domestic applications. In this article, this problem was addressed by investigating the hardware design configuration of a PPG device, with particular emphasis on the light source wavelength, light source brightness, number of light sources, photodetector lens shape, and sensor-to-skin contact pressure. A participant study was conducted to collect cardiovascular metric data from 110 participants with varying skin tones, which was used to determine the most promising hardware configuration of the PPG device. It was concluded that the device had little bias to skin tone, with only a 3.82 dB variance over all the skin tones tested. Full article

Diabetes, a chronic medical condition, affects millions of people worldwide and requires consistent monitoring of blood glucose levels (BGLs). Traditional invasive methods for BGL monitoring can be challenging and painful for patients. This study introduces a non-invasive, deep learning (DL)-based approach to estimate BGL using photoplethysmography (PPG) signals. Specifically, a Deep Sparse Capsule Network (DSCNet) model is proposed to provide accurate and robust BGL monitoring. The proposed model’s workflow includes data collection, preprocessing, feature extraction, and predictions. A hardware module was designed using a PPG sensor and Raspberry Pi to collect patient data. In preprocessing, a Savitzky–Golay filter and moving average filter were applied to remove noise and preserve pulse form and high-frequency components. The DSCNet model was then applied to predict the sugar level. Two models were developed for prediction: a baseline model, DSCNet, and an enhanced model, DSCNet with self-attention. DSCNet’s performance was evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Relative Difference (MARD), and coefficient of determination (R²), yielding values of 3.022, 0.05, 0.058, 0.062, 10.81, and 0.98, respectively. Full article